These are a collection of techniques that address the lack of
"hard data" or firm interpretations of data through the use experts.

The Success-Likelihood Index Methodology (SLIM, SLIM-MAUD) [74][75] examines
performance shaping factors (PSFs) and establishes both ratings and weight for
each PSF. SLIM-MAUD is a personal computer implementation of SLIM. The MAUD
acronym refers to "Multi-Attribute Utility Decomposition." MAUD is a
proprietary stand alone software package that aids the user in assessing
alternatives. PSFs that can be considered are:

SLIM ranks the most important PSFs. The products of the rating
and the normalized weight for each task are added to obtain the SLI (described
earlier). The SLI is related to the task success probability through the
following calibration equation:

ln

P(success) = a*SLI + b

where a and b are empirical constants. Rosa et al. [76] notes that
SLIM-MAUD requires that tasks be sorted into subsets of 4 to 10 tasks that are
similarly affected by a proposed set of PSFs. The weighting and ranking of
tasks is accomplished by a group of experts, usually four in number, who are
led by a facilitator. Analysis is conducted with the aid of the MAUD computer
program.

Rosa et al. noted many positive characteristics of SLIM-MAUD,
including face validity, practicality, estimates with acceptable levels of
reliability, ease of use and understanding, and ability to identify which PSFs
have the most effect on the SLIs. Guassardo [65] notes that
SLIM results are highly dependent on the boundary conditions used to find the
calibration equation coefficients. Poucet [64] indicates that the SLIM results
were highly dependent on the calibration reference points. The use of SLIM is
recommended only if good reference data are available. Poucet also notes that
SLIM does not address relationships among PSFs when such
exist.